Surveillance and Monitoring
There are many applications where it is important to identify or classify people, animals, vehicles, etc. (generally, objects), that may be moving in an environment. It is also useful to detect unusual patterns of motion or behavior. Such applications include surveillance and monitoring. Surveillance includes monitoring environments for suspicious activity. Machine monitoring can anticipate failures by detecting abnormal activity. Other applications can be similarly identified.
The bulk of current technologies for such monitoring typically involve the use of high-resolution, but expensive sensors such as cameras and microphone arrays. These types of sensors can provide high-resolution information about the activities in the monitored environment.
Alternatively, low-resolution and inexpensive sensors, such as motion sensors and infrared sensors, can be used. Low-resolution sensors provide coarse information such as the presence of some motion activity in the monitored environment. However, low-resolution sensors are unable to provide any additional information by themselves that permit more detailed inferences.
Doppler Signals
Doppler radar and sonar systems have been used extensively to detect moving objects. Conventionally, Doppler systems emit a broadband signal, e.g., a wide range of frequencies, as short intermittent pulses. An energy and delay of reflected pulses are indicative of a velocity and size of moving objects.
The Doppler Effect
The Doppler Effect is achieved when an oscillatory signal is incident on an object. The reflected signal has a different frequency when the object is moving than when the object is static. The Doppler Effect is commonly used for detection and ranging in sonar and radar systems.
A corollary of the basic Doppler Effect is that when the oscillatory signal is concurrently incident on multiple objects, each object reflects a signal with a different frequency based on the velocity of the object. If the oscillatory signal is incident on a collection of moving objects, then the reflected signal includes a combination of all the frequencies due to the different motions of the objects.
The Doppler Effect and Articulated Objects
An articulated object is an object with one or more rigid links, e.g., arms and legs, connected by joints having constrained movement, e.g., elbows and knees. When articulated objects move, the different links exhibit different velocities with respect to the center of mass of the object. Thus, for example, while the object, as a whole, is moving forward at three meters per second with respect to a sensor, the links can appear to be moving at various forward and backward velocities. Typically, a maximum variation in velocity is observed at the extremities of the links. When the moving object is a walking person, the velocities vary in a generally predictable oscillatory manner.
When an oscillatory signal is incident on an articulated object, different parts of the object having different velocities will reflect different frequencies. Thus, the spectrum of the signal captured by a sensor exhibits an entire range of frequencies. Further, the spectrum varies continually over time as the velocities of the parts of the object change. This variation may be periodic or cyclic when the motion of the articulated object is periodic.
When a continuous tone, either acoustic or electromagnetic, is incident on the moving articulated object, the spectrum of the reflected signal shows patterns and periodicities that are characteristic of the motion of the target object.
Gait Analysis
We note finally that the human body may be modeled as an articulated object consisting of joints and stiff connections between them. A person's gait is the characteristic motion of their underlying articulated structure.
Because of the predictability of human gait, rule-based systems have been used to identify gaits. This makes sense. Hence, the Doppler shifted spectrum of a tone reflected from a moving person can be used to identify the person by their gait, M. Otero, “Application of a continuous wave radar for human gait recognition”, Proc. of SPIE, vol. 5809, pp. 538-548, March 2005 and J. Geisheimer et al., “A continuous-wave (CW) radar for gait analysis”, Conference Record of the Thirty-Fifth Asilomar Conference on Signals, Systems and Computers, vol. 1, pp. 834-838, 4-7 Nov. 2001.
In the prior art, the reflected Doppler signal from a target object is analyzed. Features that are specific to gait are extracted. Then, a rule-based classifier designed for gait features is used to identify the presence of a person, or to distinguish between a person and an animal according to their gait. Such systems generally give reliable results.
However, there are a number of problems with the prior art Doppler systems. First, the features that are extracted are features that are consistent with a specific motion, particularly oscillatory motion of an articulated animate object. Thus, effectively, the type of motion and the type of object is generally known. This makes it possible to use a rule-based system, which gives reliable classifications. However, a rule-based system for specific features is constrained to identify a specific object or a narrow class of objects, with a particular type of motion. Specifically, up to now, only animal and human oscillatory gaits can be identified with a continuous wave Doppler system.